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On Time Trend of COVID-19: A Panel Data Study

Author

Listed:
  • Dong, C.
  • Gao, J.
  • Linton, O.
  • Peng, B.

Abstract

In this paper, we study the trending behaviour of COVID-19 data at country level, and draw attention to some existing econometric tools which are potentially helpful to understand the trend better in future studies. In our empirical study, we find that European countries overall flatten the curves more effectively compared to the other regions, while Asia & Oceania also achieve some success, but the situations are not as optimistic as in Europe. Africa and America are still facing serious challenges in terms of managing the spread of the virus and reducing the death rate. In Africa, the rate of the spread of the virus is slower and the death rate is also lower than those of the other regions. By comparing the performances of different countries, our results on the performance of different countries in managing the speed of the virus agree with Gu et al. (2020). For example, both studies agree that countries such as USA, UK and Italy perform relatively poorly; on the other hand, Australia, China, Japan, Korea, and Singapore perform relatively better.

Suggested Citation

  • Dong, C. & Gao, J. & Linton, O. & Peng, B., 2020. "On Time Trend of COVID-19: A Panel Data Study," Cambridge Working Papers in Economics 2065, Faculty of Economics, University of Cambridge.
    • Handle: RePEc:cam:camdae:2065
    Note: obl20
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    References listed on IDEAS

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    1. Yongmiao Hong, 2005. "Nonparametric Specification Testing for Continuous-Time Models with Applications to Term Structure of Interest Rates," The Review of Financial Studies, Society for Financial Studies, vol. 18(1), pages 37-84.
    2. Liu, Laura & Moon, Hyungsik Roger & Schorfheide, Frank, 2021. "Panel forecasts of country-level Covid-19 infections," Journal of Econometrics, Elsevier, vol. 220(1), pages 2-22.
    3. Gao, Jiti & Linton, Oliver & Peng, Bin, 2020. "Inference On A Semiparametric Model With Global Power Law And Local Nonparametric Trends," Econometric Theory, Cambridge University Press, vol. 36(2), pages 223-249, April.
    4. Li, Shaoran & Linton, Oliver, 2021. "When will the Covid-19 pandemic peak?," Journal of Econometrics, Elsevier, vol. 220(1), pages 130-157.
    5. Qi Li & Jeffrey Scott Racine, 2006. "Density Estimation, from Nonparametric Econometrics: Theory and Practice," Introductory Chapters, in: Nonparametric Econometrics: Theory and Practice, Princeton University Press.
    6. Jin, Sainan & Miao, Ke & Su, Liangjun, 2021. "On factor models with random missing: EM estimation, inference, and cross validation," Journal of Econometrics, Elsevier, vol. 222(1), pages 745-777.
    7. Qi Li & Jeffrey Scott Racine, 2006. "Nonparametric Econometrics: Theory and Practice," Economics Books, Princeton University Press, edition 1, volume 1, number 8355.
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    RePEc Biblio mentions

    As found on the RePEc Biblio, the curated bibliography for Economics:
    1. > Economics of Welfare > Health Economics > Economics of Pandemics > Specific pandemics > Covid-19 > Modelling > Statistical Modelling

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    Cited by:

    1. Giovanni Angelini & Giuseppe Cavaliere & Enzo D'Innocenzo & Luca De Angelis, 2022. "Time-Varying Poisson Autoregression," Papers 2207.11003, arXiv.org.

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    More about this item

    Keywords

    COVID-19; Deterministic time trend; Panel data; Varying-coefficient;
    All these keywords.

    JEL classification:

    • C23 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Models with Panel Data; Spatio-temporal Models
    • C54 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Quantitative Policy Modeling

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